Structure and Dynamics of Type 4a Pili and Type 2 Secretion System Endopili.

Within the highly diverse type four filament (TFF or T4F) superfamily, the machineries of type IVa pili (T4aP) and the type 2 secretion system (T2SS) in diderm bacteria exhibit a substantial sequence similarity despite divergent functions and distinct appearances: T4aP can extend micrometers beyond the outer membrane, whereas the endopili in the T2SS are restricted to the periplasm. The determination of the structure of individual components and entire filaments is crucial to understand how their structure enables them to serve different functions. However, the dynamics of these filaments poses a challenge for their high-resolution structure determination. This review presents different approaches that have been used to study the structure and dynamics of T4aP and T2SS endopili by means of integrative structural biology, cryo-electron microscopy (cryo-EM), and molecular dynamics simulations. Their conserved features and differences are presented. The non-helical stretch in the long-conserved N-terminal helix which is characteristic of all members of the TFF and the impact of calcium on structure, function, and dynamics of these filaments are discussed in detail.

Protein interaction explorer (PIE): a comprehensive platform for navigating protein-protein interactions and ligand binding pockets.

SUMMARY: Protein Interaction Explorer (PIE) is a new web-based tool integrated to our database iPPI-DB, specifically crafted to support structure-based drug discovery initiatives focused on protein-protein interactions (PPIs). Drawing upon extensive structural data encompassing thousands of heterodimer complexes, including those with successful ligands, PIE provides a comprehensive suite of tools dedicated to aid decision-making in PPI drug discovery. PIE enables researchers/bioinformaticians to identify and characterize crucial factors such as the presence of binding pockets or functional binding sites at the interface, predicting hot spots, and foreseeing similar protein-embedded pockets for potential repurposing efforts. AVAILABILITY AND IMPLEMENTATION: PIE is user-friendly and readily accessible at https://ippidb.pasteur.fr/targetcentric/. It relies on the NGL visualizer.

The hinge-engineered IgG1-IgG3 hybrid subclass IgGh47 potently enhances Fc-mediated function of anti-streptococcal and SARS-CoV-2 antibodies.

Streptococcus pyogenes can cause invasive disease with high mortality despite adequate antibiotic treatments. To address this unmet need, we have previously generated an opsonic IgG1 monoclonal antibody, Ab25, targeting the bacterial M protein. Here, we engineer the IgG2-4 subclasses of Ab25. Despite having reduced binding, the IgG3 version promotes stronger phagocytosis of bacteria. Using atomic simulations, we show that IgG3's Fc tail has extensive movement in 3D space due to its extended hinge region, possibly facilitating interactions with immune cells. We replaced the hinge of IgG1 with four different IgG3-hinge segment subclasses, IgGhxx. Hinge-engineering does not diminish binding as with IgG3 but enhances opsonic function, where a 47 amino acid hinge is comparable to IgG3 in function. IgGh47 shows improved protection against S. pyogenes in a systemic infection mouse model, suggesting that IgGh47 has promise as a preclinical therapeutic candidate. Importantly, the enhanced opsonic function of IgGh47 is generalizable to diverse S. pyogenes strains from clinical isolates. We generated IgGh47 versions of anti-SARS-CoV-2 mAbs to broaden the biological applicability, and these also exhibit strongly enhanced opsonic function compared to the IgG1 subclass. The improved function of the IgGh47 subclass in two distant biological systems provides new insights into antibody function.

A comprehensive dataset of protein-protein interactions and ligand binding pockets for advancing drug discovery.

This dataset represents a collection of pocket-centric structural data related to protein-protein interactions (PPIs) and PPI-related ligand binding sites. The dataset includes high-quality structural information on more than 23,000 pockets, 3,700 proteins on more than 500 organisms, and nearly 3500 ligands that can aid researchers in the fields of bioinformatics, structural biology, and drug discovery. It encompasses a diverse set of PPI complexes with more than 1,700 unique protein families including some with associated ligands, enabling detailed investigations into molecular interactions at the atomic level. This article introduces an indispensable resource designed to unlock the full potential of PPIs while pioneering a novel metric for pocket similarity for hypothesizing protein partners repurposing.

AlignScape, displaying sequence similarity using self-organizing maps.

The current richness of sequence data needs efficient methodologies to display and analyze the complexity of the information in a compact and readable manner. Traditionally, phylogenetic trees and sequence similarity networks have been used to display and analyze sequences of protein families. These methods aim to shed light on key computational biology problems such as sequence classification and functional inference. Here, we present a new methodology, AlignScape, based on self-organizing maps. AlignScape is applied to three large families of proteins: the kinases and GPCRs from human, and bacterial T6SS proteins. AlignScape provides a map of the similarity landscape and a tree representation of multiple sequence alignments These representations are useful to display, cluster, and classify sequences as well as identify functional trends. The efficient GPU implementation of AlignScape allows the analysis of large MSAs in a few minutes. Furthermore, we show how the AlignScape analysis of proteins belonging to the T6SS complex can be used to predict coevolving partners.

Acinetobacter type VI secretion system comprises a non-canonical membrane complex.

A. baumannii can rapidly acquire new resistance mechanisms and persist on abiotic surface, enabling the colonization of asymptomatic human host. In Acinetobacter the type VI secretion system (T6SS) is involved in twitching, surface motility and is used for interbacterial competition allowing the bacteria to uptake DNA. A. baumannii possesses a T6SS that has been well studied for its regulation and specific activity, but little is known concerning its assembly and architecture. The T6SS nanomachine is built from three architectural sub-complexes. Unlike the baseplate (BP) and the tail-tube complex (TTC), which are inherited from bacteriophages, the membrane complex (MC) originates from bacteria. The MC is the most external part of the T6SS and, as such, is subjected to evolution and adaptation. One unanswered question on the MC is how such a gigantesque molecular edifice is inserted and crosses the bacterial cell envelope. The A. baumannii MC lacks an essential component, the TssJ lipoprotein, which anchors the MC to the outer membrane. In this work, we studied how A. baumannii compensates the absence of a TssJ. We have characterized for the first time the A. baumannii's specific T6SS MC, its unique characteristic, its membrane localization, and assembly dynamics. We also defined its composition, demonstrating that its biogenesis employs three Acinetobacter-specific envelope-associated proteins that define an intricate network leading to the assembly of a five-proteins membrane super-complex. Our data suggest that A. baumannii has divided the function of TssJ by (1) co-opting a new protein TsmK that stabilizes the MC and by (2) evolving a new domain in TssM for homo-oligomerization, a prerequisite to build the T6SS channel. We believe that the atypical species-specific features we report in this study will have profound implication in our understanding of the assembly and evolutionary diversity of different T6SSs, that warrants future investigation.

Structure and dynamic association of an assembly platform subcomplex of the bacterial type II secretion system.

Type II secretion systems (T2SSs) allow diderm bacteria to secrete hydrolytic enzymes, adhesins, or toxins important for growth and virulence. To promote secretion of folded proteins, T2SSs assemble periplasmic filaments called pseudopili or endopili at an inner membrane subcomplex, the assembly platform (AP). Here, we combined biophysical approaches, nuclear magnetic resonance (NMR) and X-ray crystallography, to study the Klebsiella AP components PulL and PulM. We determined the structure and associations of their periplasmic domains and describe the structure of the heterodimer formed by their ferredoxin-like domains. We show how structural complementarity and plasticity favor their association during the secretion process. Cysteine scanning and crosslinking data provided additional constraints to build a structural model of the PulL-PulM assembly in the cellular context. Our structural and functional insights, together with the relative cellular abundance of its components, support the role of AP as a dynamic hub that orchestrates pilus polymerization.

Building Protein Atomic Models from Cryo-EM Density Maps and Residue Co-Evolution.

Electron cryo-microscopy (cryo-EM) has emerged as a powerful method by which to obtain three-dimensional (3D) structures of macromolecular complexes at atomic or near-atomic resolution. However, de novo building of atomic models from near-atomic resolution (3-5 Å) cryo-EM density maps is a challenging task, in particular because poorly resolved side-chain densities hamper sequence assignment by automatic procedures at a lower resolution. Furthermore, segmentation of EM density maps into individual subunits remains a difficult problem when the structure of the subunits is not known, or when significant conformational rearrangement occurs between the isolated and associated form of the subunits. To tackle these issues, we have developed a graph-based method to thread most of the C-α trace of the protein backbone into the EM density map. The EM density is described as a weighted graph such that the resulting minimum spanning tree encompasses the high-density regions of the map. A pruning algorithm cleans the tree and finds the most probable positions of the C-α atoms, by using side-chain density when available, as a collection of C-α trace fragments. By complementing experimental EM maps with contact predictions from sequence co-evolutionary information, we demonstrate that this approach can correctly segment EM maps into individual subunits and assign amino acid sequences to backbone traces to generate atomic models.

Enzymatic and Molecular Characterization of Anti-Leishmania Molecules That Differently Target Leishmania and Mammalian eIF4A Proteins, LieIF4A and eIF4AMus.

Previous investigations of the Leishmania infantum eIF4A-like protein (LieIF4A) as a potential drug target delivered cholestanol derivatives inhibitors. Here, we investigated the mode of action of cholesterol derivatives as a novel scaffold structure of LieIF4A inhibitors on the RNA-dependent ATPase activity of LieIF4A and its mammalian ortholog (eIF4AI). We compared their biochemical effects on RNA-dependent ATPase activities of both proteins and investigated if rocaglamide, a known inhibitor of eIF4A, could affect LieIF4A as well. Kinetic measurements were conducted at different concentrations of ATP, of the compound and in the presence of saturating whole yeast RNA concentrations. Kinetic analyses showed different ATP binding affinities for the two enzymes as well as different sensitivities to 7-α-aminocholesterol and rocaglamide. The 7-α-aminocholesterol inhibited LieIF4A with a higher binding affinity relative to cholestanol analogs. Cholesterol, another tested sterol, had no effect on the ATPase activity of LieIF4A or eIF4AI. The 7-α-aminocholesterol demonstrated an anti-Leishmania activity on L. infantum promastigotes. Additionally, docking simulations explained the importance of the double bond between C5 and C6 in 7-α-aminocholesterol and the amino group in the C7 position. In conclusion, Leishmania and mammalian eIF4A proteins appeared to interact differently with effectors, thus making LieIF4A a potential drug against leishmaniases.

Secondary structure and 1H, 15 N & 13C resonance assignments of the periplasmic domain of OutG, major pseudopilin from Dickeya dadantii type II secretion system.

The ability to interact and adapt to the surrounding environment is vital for bacteria that colonise various niches and organisms. One strategy developed by Gram-negative bacteria is to secrete exoprotein substrates via the type II secretion system (T2SS). The T2SS is a proteinaceous complex spanning the bacterial envelope that translocates folded proteins such as toxins and enzymes from the periplasm to the extracellular milieu. In the T2SS, a cytoplasmic ATPase elongates in the periplasm the pseudopilus, a non-covalent polymer composed of protein subunits named pseudopilins, and anchored in the inner membrane by a transmembrane helix. The pseudopilus polymerisation is coupled to the secretion of substrates. The T2SS of Dickeya dadantii secretes more than 15 substrates, essentially plant cell wall degrading enzymes. In D. dadantii, the major pseudopilin or the major subunit of the pseudopilus is called OutG. To better understand the mechanism of secretion of these numerous substrates via the pseudopilus, we have been studying the structure of OutG by NMR. Here, as the first part of this study, we report the 1H, 15N and 13C backbone and sidechain chemical shift assignment of the periplasmic domain of OutG and its NMR derived secondary structure.

Bat coronaviruses related to SARS-CoV-2 and infectious for human cells.

The animal reservoir of SARS-CoV-2 is unknown despite reports of SARS-CoV-2-related viruses in Asian Rhinolophus bats1-4, including the closest virus from R. affinis, RaTG13 (refs. 5,6), and pangolins7-9. SARS-CoV-2 has a mosaic genome, to which different progenitors contribute. The spike sequence determines the binding affinity and accessibility of its receptor-binding domain to the cellular angiotensin-converting enzyme 2 (ACE2) receptor and is responsible for host range10-12. SARS-CoV-2 progenitor bat viruses genetically close to SARS-CoV-2 and able to enter human cells through a human ACE2 (hACE2) pathway have not yet been identified, although they would be key in understanding the origin of the epidemic. Here we show that such viruses circulate in cave bats living in the limestone karstic terrain in northern Laos, in the Indochinese peninsula. We found that the receptor-binding domains of these viruses differ from that of SARS-CoV-2 by only one or two residues at the interface with ACE2, bind more efficiently to the hACE2 protein than that of the SARS-CoV-2 strain isolated in Wuhan from early human cases, and mediate hACE2-dependent entry and replication in human cells, which is inhibited by antibodies that neutralize SARS-CoV-2. None of these bat viruses contains a furin cleavage site in the spike protein. Our findings therefore indicate that bat-borne SARS-CoV-2-like viruses that are potentially infectious for humans circulate in Rhinolophus spp. in the Indochinese peninsula.

InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein-protein interactions.

MOTIVATION: Protein-protein interactions (PPIs) are key elements in numerous biological pathways and the subject of a growing number of drug discovery projects including against infectious diseases. Designing drugs on PPI targets remains a difficult task and requires extensive efforts to qualify a given interaction as an eligible target. To this end, besides the evident need to determine the role of PPIs in disease-associated pathways and their experimental characterization as therapeutics targets, prediction of their capacity to be bound by other protein partners or modulated by future drugs is of primary importance. RESULTS: We present InDeep, a tool for predicting functional binding sites within proteins that could either host protein epitopes or future drugs. Leveraging deep learning on a curated dataset of PPIs, this tool can proceed to enhanced functional binding site predictions either on experimental structures or along molecular dynamics trajectories. The benchmark of InDeep demonstrates that our tool outperforms state-of-the-art ligandable binding sites predictors when assessing PPI targets but also conventional targets. This offers new opportunities to assist drug design projects on PPIs by identifying pertinent binding pockets at or in the vicinity of PPI interfaces. AVAILABILITY AND IMPLEMENTATION: The tool is available on GitLab at https://gitlab.pasteur.fr/InDeep/InDeep. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational and biochemical analysis of type IV pilus dynamics and stability.

Type IV pili (T4P) are distinctive dynamic filaments at the surface of many bacteria that can rapidly extend and retract and withstand strong forces. T4P are important virulence factors in many human pathogens, including Enterohemorrhagic Escherichia coli (EHEC). The structure of the EHEC T4P has been determined by integrating nuclear magnetic resonance (NMR) and cryo-electron microscopy data. To better understand pilus assembly, stability, and function, we performed a total of 108 ms all-atom molecular dynamics simulations of wild-type and mutant T4P. Extensive characterization of the conformational landscape of T4P in different conditions of temperature, pH, and ionic strength is complemented with targeted mutagenesis and biochemical analyses. Our simulations and NMR experiments reveal a conserved set of residues defining a calcium-binding site at the interface between three pilin subunits. Calcium binding enhances T4P stability ex vivo and in vitro, supporting the role of this binding site as a potential pocket for drug design.

Host-Pathogen Adhesion as the Basis of Innovative Diagnostics for Emerging Pathogens.

Infectious diseases are an existential health threat, potentiated by emerging and re-emerging viruses and increasing bacterial antibiotic resistance. Targeted treatment of infectious diseases requires precision diagnostics, especially in cases where broad-range therapeutics such as antibiotics fail. There is thus an increasing need for new approaches to develop sensitive and specific in vitro diagnostic (IVD) tests. Basic science and translational research are needed to identify key microbial molecules as diagnostic targets, to identify relevant host counterparts, and to use this knowledge in developing or improving IVD. In this regard, an overlooked feature is the capacity of pathogens to adhere specifically to host cells and tissues. The molecular entities relevant for pathogen-surface interaction are the so-called adhesins. Adhesins vary from protein compounds to (poly-)saccharides or lipid structures that interact with eukaryotic host cell matrix molecules and receptors. Such interactions co-define the specificity and sensitivity of a diagnostic test. Currently, adhesin-receptor binding is typically used in the pre-analytical phase of IVD tests, focusing on pathogen enrichment. Further exploration of adhesin-ligand interaction, supported by present high-throughput "omics" technologies, might stimulate a new generation of broadly applicable pathogen detection and characterization tools. This review describes recent results of novel structure-defining technologies allowing for detailed molecular analysis of adhesins, their receptors and complexes. Since the host ligands evolve slowly, the corresponding adhesin interaction is under selective pressure to maintain a constant receptor binding domain. IVD should exploit such conserved binding sites and, in particular, use the human ligand to enrich the pathogen. We provide an inventory of methods based on adhesion factors and pathogen attachment mechanisms, which can also be of relevance to currently emerging pathogens, including SARS-CoV-2, the causative agent of COVID-19.

1H, 15 N and 13C resonance assignments of the C-terminal domain of PulL, a component of the Klebsiella oxytoca type II secretion system.

Type II secretion systems (T2SS) allow Gram-negative bacteria to transport toxins and enzymes from the periplasm to the external milieu, and are thus important for the pathogenicity of bacteria. To drive secretion, T2SS assemble filaments called pseudopili closely related to bacterial type IV pili. These filaments are non-covalent polymers of proteins that are assembled by an inner membrane complex called the assembly platform connected to a cytoplasmic ATPase motor. In the Klebsiella oxytoca T2SS, the PulL protein from the assembly platform is essential for pseudopilus assembly and protein secretion. However, its role in these processes is not well understood. To decipher the molecular basis of PulL function, we used solution NMR to study its structure and interactions with other components of the machinery. Here as a first step, we report the 1H, 15 N and 13C backbone and side-chain chemical shift assignments of the C-terminal periplasmic domain of PulL and its secondary structure based on NMR data.

Automatic Bayesian Weighting for SAXS Data.

Small-angle X-ray scattering (SAXS) experiments are important in structural biology because they are solution methods, and do not require crystallization of protein complexes. Structure determination from SAXS data, however, poses some difficulties. Computation of a SAXS profile from a protein model is expensive in CPU time. Hence, rather than directly refining against the data, most computational methods generate a large number of conformers and then filter the structures based on how well they satisfy the SAXS data. To address this issue in an efficient manner, we propose here a Bayesian model for SAXS data and use it to directly drive a Monte Carlo simulation. We show that the automatic weighting of SAXS data is the key to finding optimal structures efficiently. Another key problem with obtaining structures from SAXS data is that proteins are often flexible and the data represents an average over a structural ensemble. To address this issue, we first characterize the stability of the best model with extensive molecular dynamics simulations. We analyse the resulting trajectories further to characterize a dynamic structural ensemble satisfying the SAXS data. The combination of methods is applied to a tandem of domains from the protein PTPN4, which are connected by an unstructured linker. We show that the SAXS data contain information that supports and extends other experimental findings. We also show that the conformation obtained by the Bayesian analysis is stable, but that a minor conformation is present. We propose a mechanism in which the linker may maintain PTPN4 in an inhibited enzymatic state.

The iPPI-DB initiative: a community-centered database of protein-protein interaction modulators.

MOTIVATION: One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets. RESULTS: Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets and extended our focus to stabilizers of PPIs as well. AVAILABILITY AND IMPLEMENTATION: The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structural determination of Streptococcus pyogenes M1 protein interactions with human immunoglobulin G using integrative structural biology.

Streptococcus pyogenes (Group A streptococcus; GAS) is an important human pathogen responsible for mild to severe, life-threatening infections. GAS expresses a wide range of virulence factors, including the M family proteins. The M proteins allow the bacteria to evade parts of the human immune defenses by triggering the formation of a dense coat of plasma proteins surrounding the bacteria, including IgGs. However, the molecular level details of the M1-IgG interaction have remained unclear. Here, we characterized the structure and dynamics of this interaction interface in human plasma on the surface of live bacteria using integrative structural biology, combining cross-linking mass spectrometry and molecular dynamics (MD) simulations. We show that the primary interaction is formed between the S-domain of M1 and the conserved IgG Fc-domain. In addition, we show evidence for a so far uncharacterized interaction between the A-domain and the IgG Fc-domain. Both these interactions mimic the protein G-IgG interface of group C and G streptococcus. These findings underline a conserved scavenging mechanism used by GAS surface proteins that block the IgG-receptor (FcγR) to inhibit phagocytic killing. We additionally show that we can capture Fab-bound IgGs in a complex background and identify XLs between the constant region of the Fab-domain and certain regions of the M1 protein engaged in the Fab-mediated binding. Our results elucidate the M1-IgG interaction network involved in inhibition of phagocytosis and reveal important M1 peptides that can be further investigated as future vaccine targets.

quicksom: Self-Organizing Maps on GPUs for clustering of molecular dynamics trajectories.

SUMMARY: We implemented the Self-Organizing Maps algorithm running efficiently on GPUs, and also provide several clustering methods of the resulting maps. We provide scripts and a use case to cluster macro-molecular conformations generated by molecular dynamics simulations. AVAILABILITY AND IMPLEMENTATION: The method is available on GitHub and distributed as a pip package.